Lifeboat Foundation

Elon Musk announces that Tesla will soon be joining the robotic industry. New release of cars in 2022 will be delayed to make way for the humanoid robot development of the company.

Circa 2020 Simulation of the human brain.

Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator.

Computer simulation of the whole human brain is an ambitious challenge in the field of computational neuroscience and high-performance computing (Izhikevich, 2005; Izhikevich and Edelman, 2008; Amunts et al., 2016). The human brain contains approximately 100 billion neurons. While the cerebral cortex occupies 82% of the brain mass, it contains only 19% (16 billion) of all neurons. The cerebellum, which occupies only 10% of the brain mass, contains 80% (69 billion) of all neurons (Herculano-Houzel, 2009). Thus, we could say that 80% of human-scale whole brain simulation will be accomplished when a human-scale cerebellum is built and simulated on a computer. The human cerebellum plays crucial roles not only in motor control and learning (Ito, 1984, 2000) but also in cognitive tasks (Ito, 2012; Buckner, 2013). In particular, the human cerebellum seems to be involved in human-specific tasks, such as bipedal locomotion, natural language processing, and use of tools (Lieberman, 2014).

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Pioneer 10 was ahead of its time.

A mere fifty years ago, Pioneer 10 launched and became the first unmanned mission to Jupiter. The mission revealed new details about the Solar System — and sent a message to aliens.

Researchers with the University of Chicago Pritzker School of Molecular Engineering have shown for the first time how to design the basic elements needed for logic operations using a kind of material called a liquid crystal—paving the way for a completely novel way of performing computations.

The results, published Feb. 23 in Science Advances, are not likely to become transistors or computers right away, but the technique could point the way towards devices with new functions in sensing, computing and robotics.

“We showed you can create the elementary building blocks of a circuit—gates, amplifiers, and conductors—which means you should be able to assemble them into arrangements capable of performing more complex operations,” said Juan de Pablo, the Liew Family Professor in Molecular Engineering and senior scientist at Argonne National Laboratory, and the senior corresponding author on the paper. “It’s a really exciting step for the field of active materials.”

Autonomous drone mapping startup Emesent has announced its latest survey-grade LiDAR payload: Hovermap ST. The lightweight, IP65-rated solution is being launched with Emesent’s new Automated Ground Control feature that, the company stresses, enables autonomous data capture in harsher environments than ever and for a wider range of use cases.

Emesent’s LiDAR payloads leverage a process called simultaneous localization and mapping (SLAM), in which a drone builds a map and, at the same time, localizes the drone in that map.

The H+ Academy Roundtable presents “AGI: How it got started and key issues ahead” with Peter Voss and Ben Goertzel.

Neural networks have become a hot topic over the last decade, put to work on jobs from recognizing image content to generating text and even playing video games. However, these artificial neural networks are essentially just piles of maths inside a computer, and while they are capable of great things, the technology hasn’t yet shown the capability to produce genuine intelligence.

Cortical Labs, based down in Melbourne, Australia, has a different approach. Rather than rely solely on silicon, their work involves growing real biological neurons on electrode arrays, allowing them to be interfaced with digital systems. Their latest work has shown promise that these real biological neural networks can be made to learn, according to a pre-print paper that is yet to go through peer review.

The broad aim of the work is to harness biological neurons for their computational power, in an attempt to create “synthetic biological intelligence”. The general idea is that biological neurons have far more complexity and capability than any neural networks simulated in software. Thus, if one wishes to create a viable intelligence from scratch, it makes more sense to use biological neurons rather than messing about with human-created simulations of such.